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基于二次分解整合模型的Microcystis aeruginosa的生长预测

Juan Huan¹, Beier Yang², Mingbao Li²

¹School of Computer Science and Artificial Intelligence, Aliyun School of Big Data, School of Software, Changzhou University, Changzhou 213164, Jiangsu, China

Water science and technology : a journal of the International Association on Water Pollution Research

|August 31, 2023

概括

这项研究引入了一种新的WD-VMD-GRU模型,通过分析叶绿素-a水平来预测Microcystis aeruginosa的开花. 先进的分解技术显著提高了对有害藻类繁殖的预测准确度.

科学领域:

环境科学环境科学
生态生态学生态生态学
数据科学数据科学数据科学

背景情况:

像太湖湖这样的温湖中的微囊气球菌 (Microcystis aeruginosa) 开花带来了生态挑战.
叶绿素a是监测Microcystis aeruginosa生物量的关键指标.
叶绿素a数据的非线性和不稳定性阻碍了精确的开花预测.

研究的目的:

开发一个更准确的Microcystis aeruginosa生物质预测模型.
为应对非线性和不稳定的甲基序列所带来的挑战.
为了提高科学基础,预测有害的藻类繁殖.

主要方法:

开发了一种二次分解预测方法,将波纹分解 (WD),变量模态分解 (VMD) 和门式循环单位 (GRU) 整合起来.
原始的叶绿素-a序列首先使用WD分解,其次是VMD在高成分上.
使用GRU预测了单个组件,并重建了最终预测的结果.

主要成果:

与其他方法相比,WD-VMD-GRU模型显示出更高的分解效率 (VMD > WD > CEEMDAN > EMD).
与基本模型相比,该模型在R平方 (R2) 中取得了6.5%以上的显著改进.
在RMSE,MAE和R2值为1.752,1.450和0.969的2天预测,以及3.169,2.711和0.908的6天预测时,获得了准确的预测.

结论:

二次分解方法有效地降低了预测的复杂性,并提高了性能.
WD-VMD-GRU模型为Microcystis aeruginosa生长预测提供了卓越的准确性.
该模型为监测和管理有害藻类繁殖提供了坚实的科学基础.

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